Real-Time Semantic Mapping for Autonomous Off-Road Navigation

Maturana, Daniel; Chou, Po-Wei; Uenoyama, Masashi; Scherer, Sebastian

doi:10.1007/978-3-319-67361-5_22

Cited by 148 publications

(124 citation statements)

References 19 publications

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“…Despite these drawbacks, there is no alternative to HD maps for navigation in the current state of technology . In regards to localizing the vehicle without high precision GPS signals, techniques have been proposed in recent years but still rely heavily on prior detailed map information, or remain limited in terms of real‐time mapping of the full roadway scene . Finally, SLAM techniques, such as FAB‐MAP and SeqSLAM, can be used to alleviate the need for HD maps and GPS signals .…”

Section: Resultsmentioning

confidence: 99%

“…14 In regards to localizing the vehicle without high precision GPS signals, techniques have been proposed in recent years but still rely heavily on prior detailed map information, [20][21][22][23][24] or remain limited in terms of real-time mapping of the full roadway scene. [26][27][28][29] Finally, SLAM techniques, such as FAB-MAP and SeqSLAM, can be used to alleviate the need for HD maps and GPS signals. 15,16 However, these methods do not attempt to localize the vehicle in metric coordinates, and therefore require a dense map of images to adequately localize the vehicle.…”

Section: Resultsmentioning

confidence: 99%

“…Through the use of this algorithm, a converged and accurate position estimate was retained during an 8‐minute GPS blackout. Alternative methods, which take a perception approach, demonstrate the ability to automatically map the 3D position of traffic lights, enable real‐time autonomous off‐road navigation using semantic mapping, and allow for real‐time joint semantic reasoning for road scene understanding . Additionally, a study has been shown to provide self‐localization through the use of visual odometry along with map data from OpenStreetMap .…”

Section: Introductionmentioning

confidence: 99%

“…Alternative methods, which take a perception approach, demonstrate the ability to automatically map the 3D position of traffic lights, enable real-time autonomous off-road navigation using semantic mapping, and allow for real-time joint semantic reasoning for road scene understanding. [26][27][28] Additionally, a study has been shown to provide self-localization through the use of visual odometry along with map data from OpenStreetMap. 29 These techniques address key mapping and localization challenges; however, they remain limited in terms of real-time mapping and do not provide the capability to infer the full roadway intersection scene required for navigation.…”

Section: Introductionmentioning

confidence: 99%

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Estimation and navigation methods with limited information for autonomous urban driving

Chipka

Campbell

2019

Engineering Reports

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Autonomous driving in dense urban areas presents an especially difficult task. First, globally localizing information, such as GPS signal, often proves to be unreliable in such areas due to signal shadowing and multipath errors. Second, the high‐definition environmental maps with sufficient information for autonomous navigation require a large amount of data to be collected from these areas, significant postprocessing of this data to generate the map, and then continual maintenance of the map to account for changes in the environment. This paper addresses the issue of autonomous driving in urban environments by investigating algorithms and an architecture to enable fully functional autonomous driving with little to no reliance on map‐based measurements or GPS signals. An extended Kalman filter with odometry, compass, and sparse landmark measurements as inputs is used to provide localization. Real‐time detection and estimation of key roadway features are used to create an understanding of the surrounding static scene. Navigation is accomplished by a compass‐based navigation control law. Experimental scene understanding results are obtained using computer vision and estimation techniques and demonstrate the ability to probabilistically infer key features of an intersection in real time. Key results from Monte Carlo studies demonstrate the proposed localization and navigation methods. These tests provide success rates of urban navigation under different environmental conditions, such as landmark density, and show that the vehicle can navigate to a goal nearly 10 km away without any external pose update at all. Field tests validate these simulated results and demonstrate that, for given test conditions, an expected range can be determined for a given success rate.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation and navigation methods with limited information for autonomous urban driving

Chipka

Campbell

2019

Engineering Reports

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“…In addition, it can improve the efficiency of resource utilization. Effectively labeling the correct class for all points in the scene is an important basis for the widespread adoption of point clouds [1][2][3][4]. However, a laser point cloud has a huge data number, high redundancy, and uneven scene distribution, which may lead to huge challenges in the point cloud classification.…”

Section: Introductionmentioning

confidence: 99%

A Single Point-Based Multilevel Features Fusion and Pyramid Neighborhood Optimization Method for ALS Point Cloud Classification

Tong

et al. 2019

Applied Sciences

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3D point cloud classification has wide applications in the field of scene understanding. Point cloud classification based on points can more accurately segment the boundary region between adjacent objects. In this paper, a point cloud classification algorithm based on a single point multilevel features fusion and pyramid neighborhood optimization are proposed for a Airborne Laser Scanning (ALS) point cloud. First, the proposed algorithm determines the neighborhood region of each point, after which the features of each single point are extracted. For the characteristics of the ALS point cloud, two new feature descriptors are proposed, i.e., a normal angle distribution histogram and latitude sampling histogram. Following this, multilevel features of a single point are constructed by multi-resolution of the point cloud and multi-neighborhood spaces. Next, the features are trained by the Support Vector Machine based on a Gaussian kernel function, and the points are classified by the trained model. Finally, a classification results optimization method based on a multi-scale pyramid neighborhood constructed by a multi-resolution point cloud is used. In the experiment, the algorithm is tested by a public dataset. The experimental results show that the proposed algorithm can effectively classify large-scale ALS point clouds. Compared with the existing algorithms, the proposed algorithm has a better classification performance.

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JEON, Beomseok (BJ): Seoul/Republic of Korea

Jeon

2019

Leadership in Movement Disorders

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This paper investigates the impact of observations on atmospheric state estimation in weather forecasting systems using graph neural networks (GNNs) and explainability methods. We integrate observation and Numerical Weather Prediction (NWP) points into a meteorological graph, extracting k-hop subgraphs centered on NWP points. Self-supervised GNNs are employed to estimate the atmospheric state by aggregating data within these k-hop radii. The study applies gradient-based explainability methods to quantify the significance of different observations in the estimation process. Evaluated with data from 11 satellite and land-based observations, the results highlight the effectiveness of visualizing the importance of observation types, enhancing the understanding and optimization of observational data in weather forecasting.

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Real-Time Semantic Mapping for Autonomous Off-Road Navigation

Cited by 148 publications

References 19 publications

Estimation and navigation methods with limited information for autonomous urban driving

Estimation and navigation methods with limited information for autonomous urban driving

A Single Point-Based Multilevel Features Fusion and Pyramid Neighborhood Optimization Method for ALS Point Cloud Classification

JEON, Beomseok (BJ): Seoul/Republic of Korea

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